In the field of automated algorithmic classification of text strings into topical hierarchies or ontologies, there is a need to quickly identify incorrect categorizations and to provide a path for improvement. Being able to rapidly analyze and improve a large dataset of classified text with limited manual intervention allows for quick release of updated datasets, and can identify and correct errors before they manifest in applications that may rely them.
Collaborative filtering solutions require observation of usage patterns over a period of time. What is needed is a method by which large datasets may be quickly tested and text strings that are poorly classified are quickly identified and correctly categorized.